ArticleGenetic epidemiology2011
Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Scalable nonlinear Cox modeling via random Fourier features with analytic uncertainty.BMC medical research methodology · 2026Article
- Kernel-based hierarchical structural component models for pathway analysis on survival phenotype.Genes & genomics · 2024Article
- BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.Briefings in bioinformatics · 2024Article
- Bayesian bi-level variable selection for genome-wide survival study.Genomics & informatics · 2023Article
- Intravesical BCG in patients with non-muscle invasive bladder cancer induces trained immunity and decreases respiratory infections.Journal for immunotherapy of cancer · 2023Observational
- How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis.Frontiers in psychiatry · 2023Article
- A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies.BMC bioinformatics · 2022Article
- Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies.Journal of translational medicine · 2021Article
- IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.PLoS computational biology · 2021Article
- A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank.American journal of human genetics · 2020Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Identifying individual risk rare variants using protein structure guided local tests (POINT).PLoS computational biology · 2019Article
- Variance components genetic association test for zero-inflated count outcomes.Genetic epidemiology · 2019Article
- Article
- An adaptive microbiome α-diversity-based association analysis method.Scientific reports · 2018Article
- Kernel machine score test for pathway analysis in the presence of semi-competing risks.Statistical methods in medical research · 2018Article
- A highly adaptive microbiome-based association test for survival traits.BMC genomics · 2018Article
- Germline polymorphisms in myeloid-associated genes are not associated with survival in glioma patients.Journal of neuro-oncology · 2018Article
- Conditional asymptotic inference for the kernel association test.Bioinformatics (Oxford, England) · 2017Article
- Variance component score test for time-course gene set analysis of longitudinal RNA-seq data.Biostatistics (Oxford, England) · 2017Article
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Authors and funding
7 authors.
Funding
Abstract
In this article, we develop a powerful test for identifying single nucleotide polymorphism (SNP)-sets that are predictive of survival with data from genome-wide association studies. We first group typed SNPs into SNP-sets based on genomic features and then apply a score test to assess the overall effect of each SNP-set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP-set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially nonlinear effects of the SNPs, SNP-SNP interactions (epistasis), and the joint effects of multiple causal variants. By simulating SNP data based on the LD structure of real genes from the HapMap project, we demonstrate that our proposed test is more powerful than the standard single SNP minimum P-value-based test for association studies with censored survival outcomes. We illustrate the proposed test with a real data application.
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